#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


# Vertex AI Python SDK is required for this module.
# Follow https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk # pylint: disable=line-too-long
# to install Vertex AI Python SDK.

import functools
import logging
from collections.abc import Callable
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
from typing import Optional
from typing import cast

import vertexai
from google.api_core.exceptions import ServerError
from google.api_core.exceptions import TooManyRequests
from google.auth.credentials import Credentials
from vertexai.language_models import TextEmbeddingInput
from vertexai.language_models import TextEmbeddingModel
from vertexai.vision_models import Image
from vertexai.vision_models import MultiModalEmbeddingModel
from vertexai.vision_models import MultiModalEmbeddingResponse
from vertexai.vision_models import Video
from vertexai.vision_models import VideoEmbedding
from vertexai.vision_models import VideoSegmentConfig

import apache_beam as beam
from apache_beam.ml.inference.base import ModelHandler
from apache_beam.ml.inference.base import RemoteModelHandler
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.rag.types import Chunk
from apache_beam.ml.rag.types import Embedding
from apache_beam.ml.transforms.base import EmbeddingsManager
from apache_beam.ml.transforms.base import EmbeddingTypeAdapter
from apache_beam.ml.transforms.base import _ImageEmbeddingHandler
from apache_beam.ml.transforms.base import _MultiModalEmbeddingHandler
from apache_beam.ml.transforms.base import _TextEmbeddingHandler

__all__ = [
    "VertexAITextEmbeddings",
    "VertexAIImageEmbeddings",
    "VertexAIMultiModalEmbeddings",
    "VertexAIMultiModalInput",
]

DEFAULT_TASK_TYPE = "RETRIEVAL_DOCUMENT"
# TODO: https://github.com/apache/beam/issues/29356
# Can this list be automatically pulled from Vertex SDK?
TASK_TYPE_INPUTS = [
    "RETRIEVAL_DOCUMENT",
    "RETRIEVAL_QUERY",
    "SEMANTIC_SIMILARITY",
    "CLASSIFICATION",
    "CLUSTERING"
]
_BATCH_SIZE = 5  # Vertex AI limits requests to 5 at a time.

LOGGER = logging.getLogger("VertexAIEmbeddings")


def _retry_on_appropriate_gcp_error(exception):
  """
  Retry filter that returns True if a returned HTTP error code is 5xx or 429.
  This is used to retry remote requests that fail, most notably 429
  (TooManyRequests.)

  Args:
    exception: the returned exception encountered during the request/response
      loop.

  Returns:
    boolean indication whether or not the exception is a Server Error (5xx) or
      a TooManyRequests (429) error.
  """
  return isinstance(exception, (TooManyRequests, ServerError))


class _VertexAITextEmbeddingHandler(RemoteModelHandler):
  """
  Note: Intended for internal use and guarantees no backwards compatibility.
  """
  def __init__(
      self,
      model_name: str,
      title: Optional[str] = None,
      task_type: str = DEFAULT_TASK_TYPE,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    vertexai.init(project=project, location=location, credentials=credentials)
    self.model_name = model_name
    if task_type not in TASK_TYPE_INPUTS:
      raise ValueError(
          f"task_type must be one of {TASK_TYPE_INPUTS}, got {task_type}")
    self.task_type = task_type
    self.title = title

    super().__init__(
        namespace='VertexAITextEmbeddingHandler',
        retry_filter=_retry_on_appropriate_gcp_error,
        **kwargs)

  def request(
      self,
      batch: Sequence[str],
      model: TextEmbeddingModel,
      inference_args: Optional[dict[str, Any]] = None):
    embeddings = []
    batch_size = _BATCH_SIZE
    for i in range(0, len(batch), batch_size):
      text_batch_strs = batch[i:i + batch_size]
      text_batch = [
          TextEmbeddingInput(
              text=text, title=self.title, task_type=self.task_type)
          for text in text_batch_strs
      ]
      embeddings_batch = model.get_embeddings(list(text_batch))
      embeddings.extend([el.values for el in embeddings_batch])
    return embeddings

  def create_client(self) -> TextEmbeddingModel:
    model = TextEmbeddingModel.from_pretrained(self.model_name)
    return model

  def __repr__(self):
    # ModelHandler is internal to the user and is not exposed.
    # Hence we need to override the __repr__ method to expose
    # the name of the class.
    return 'VertexAITextEmbeddings'


class VertexAITextEmbeddings(EmbeddingsManager):
  def __init__(
      self,
      model_name: str,
      columns: list[str],
      title: Optional[str] = None,
      task_type: str = DEFAULT_TASK_TYPE,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    """
    Embedding Config for Vertex AI Text Embedding models following
    https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings # pylint: disable=line-too-long
    Text Embeddings are generated for a batch of text using the Vertex AI SDK.
    Embeddings are returned in a list for each text in the batch. Look at
    https://cloud.google.com/vertex-ai/docs/generative-ai/learn/model-versioning#stable-versions-available.md # pylint: disable=line-too-long
    for more information on model versions and lifecycle.

    Args:
      model_name: The name of the Vertex AI Text Embedding model.
      columns: The columns containing the text to be embedded.
      task_type: The downstream task for the embeddings. Valid values are
        RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY,
        CLASSIFICATION, CLUSTERING. For more information on the task type,
        look at https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings # pylint: disable=line-too-long
      title: Identifier of the text content.
      project: The default GCP project for API calls.
      location: The default location for API calls.
      credentials: Custom credentials for API calls.
        Defaults to environment credentials.
    """
    self.model_name = model_name
    self.project = project
    self.location = location
    self.credentials = credentials
    self.title = title
    self.task_type = task_type
    self.kwargs = kwargs
    super().__init__(columns=columns, **kwargs)

  def get_model_handler(self) -> ModelHandler:
    return _VertexAITextEmbeddingHandler(
        model_name=self.model_name,
        project=self.project,
        location=self.location,
        credentials=self.credentials,
        title=self.title,
        task_type=self.task_type,
        **self.kwargs)

  def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
    return RunInference(
        model_handler=_TextEmbeddingHandler(self),
        inference_args=self.inference_args)


class _VertexAIImageEmbeddingHandler(RemoteModelHandler):
  def __init__(
      self,
      model_name: str,
      dimension: Optional[int] = None,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    vertexai.init(project=project, location=location, credentials=credentials)
    self.model_name = model_name
    self.dimension = dimension

    super().__init__(
        namespace='VertexAIImageEmbeddingHandler',
        retry_filter=_retry_on_appropriate_gcp_error,
        **kwargs)

  def request(
      self,
      imgs: Sequence[Image],
      model: MultiModalEmbeddingModel,
      inference_args: Optional[dict[str, Any]] = None):
    embeddings = []
    # Max request size for multi-modal embedding models is 1
    for img in imgs:
      prediction = model.get_embeddings(image=img, dimension=self.dimension)
      embeddings.append(prediction.image_embedding)
    return embeddings

  def create_client(self):
    model = MultiModalEmbeddingModel.from_pretrained(self.model_name)
    return model

  def __repr__(self):
    # ModelHandler is internal to the user and is not exposed.
    # Hence we need to override the __repr__ method to expose
    # the name of the class.
    return 'VertexAIImageEmbeddings'


class VertexAIImageEmbeddings(EmbeddingsManager):
  def __init__(
      self,
      model_name: str,
      columns: list[str],
      dimension: Optional[int],
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    """
    Embedding Config for Vertex AI Image Embedding models following
    https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings # pylint: disable=line-too-long
    Image Embeddings are generated for a batch of images using the Vertex AI API.
    Embeddings are returned in a list for each image in the batch. This
    transform makes remote calls to the Vertex AI service and may incur costs
    for use.

    Args:
      model_name: The name of the Vertex AI Multi-Modal Embedding model.
      columns: The columns containing the image to be embedded.
      dimension: The length of the embedding vector to generate. Must be one of
        128, 256, 512, or 1408. If not set, Vertex AI's default value is 1408.
      project: The default GCP project for API calls.
      location: The default location for API calls.
      credentials: Custom credentials for API calls.
        Defaults to environment credentials.
    """
    self.model_name = model_name
    self.project = project
    self.location = location
    self.credentials = credentials
    self.kwargs = kwargs
    if dimension is not None and dimension not in (128, 256, 512, 1408):
      raise ValueError(
          "dimension argument must be one of 128, 256, 512, or 1408")
    self.dimension = dimension
    super().__init__(columns=columns, **kwargs)

  def get_model_handler(self) -> ModelHandler:
    return _VertexAIImageEmbeddingHandler(
        model_name=self.model_name,
        dimension=self.dimension,
        project=self.project,
        location=self.location,
        credentials=self.credentials,
        **self.kwargs)

  def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
    return RunInference(
        model_handler=_ImageEmbeddingHandler(self),
        inference_args=self.inference_args)


@dataclass
class VertexImage:
  image_content: Image
  embedding: Optional[list[float]] = None


@dataclass
class VertexVideo:
  video_content: Video
  config: VideoSegmentConfig
  embeddings: Optional[list[VideoEmbedding]] = None


@dataclass
class VertexAIMultiModalInput:
  image: Optional[VertexImage] = None
  video: Optional[VertexVideo] = None
  contextual_text: Optional[Chunk] = None


class _VertexAIMultiModalEmbeddingHandler(RemoteModelHandler):
  def __init__(
      self,
      model_name: str,
      dimension: Optional[int] = None,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    vertexai.init(project=project, location=location, credentials=credentials)
    self.model_name = model_name
    self.dimension = dimension

    super().__init__(
        namespace='VertexAIMultiModelEmbeddingHandler',
        retry_filter=_retry_on_appropriate_gcp_error,
        **kwargs)

  def request(
      self,
      batch: Sequence[VertexAIMultiModalInput],
      model: MultiModalEmbeddingModel,
      inference_args: Optional[dict[str, Any]] = None):
    embeddings = []
    # Max request size for multi-modal embedding models is 1
    for input in batch:
      image_content: Optional[Image] = None
      video_content: Optional[Video] = None
      text_content: Optional[str] = None
      video_config: Optional[VideoSegmentConfig] = None

      if input.image:
        image_content = input.image.image_content
      if input.video:
        video_content = input.video.video_content
        video_config = input.video.config
      if input.contextual_text:
        text_content = input.contextual_text.content.text

      prediction = model.get_embeddings(
          image=image_content,
          video=video_content,
          contextual_text=text_content,
          dimension=self.dimension,
          video_segment_config=video_config)
      embeddings.append(prediction)
    return embeddings

  def create_client(self) -> MultiModalEmbeddingModel:
    model = MultiModalEmbeddingModel.from_pretrained(self.model_name)
    return model

  def __repr__(self):
    # ModelHandler is internal to the user and is not exposed.
    # Hence we need to override the __repr__ method to expose
    # the name of the class.
    return 'VertexAIMultiModalEmbeddings'


def _multimodal_dict_input_fn(
    image_column: Optional[str],
    video_column: Optional[str],
    text_column: Optional[str],
    batch: Sequence[dict[str, Any]]) -> list[VertexAIMultiModalInput]:
  multimodal_inputs: list[VertexAIMultiModalInput] = []
  for item in batch:
    img: Optional[VertexImage] = None
    vid: Optional[VertexVideo] = None
    text: Optional[Chunk] = None
    if image_column:
      img = item[image_column]
    if video_column:
      vid = item[video_column]
    if text_column:
      text = item[text_column]
    multimodal_inputs.append(
        VertexAIMultiModalInput(image=img, video=vid, contextual_text=text))
  return multimodal_inputs


def _multimodal_dict_output_fn(
    image_column: Optional[str],
    video_column: Optional[str],
    text_column: Optional[str],
    batch: Sequence[dict[str, Any]],
    embeddings: Sequence[MultiModalEmbeddingResponse]) -> list[dict[str, Any]]:
  results = []
  for batch_idx, item in enumerate(batch):
    mm_embedding = embeddings[batch_idx]
    if image_column:
      item[image_column].embedding = mm_embedding.image_embedding
    if video_column:
      item[video_column].embeddings = mm_embedding.video_embeddings
    if text_column:
      item[text_column].embedding = Embedding(
          dense_embedding=mm_embedding.text_embedding)
    results.append(item)
  return results


def _create_multimodal_dict_adapter(
    image_column: Optional[str],
    video_column: Optional[str],
    text_column: Optional[str]
) -> EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]]:
  return EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]](
      input_fn=cast(
          Callable[[Sequence[dict[str, Any]]], list[str]],
          functools.partial(
              _multimodal_dict_input_fn,
              image_column,
              video_column,
              text_column)),
      output_fn=cast(
          Callable[[Sequence[dict[str, Any]], Sequence[Any]],
                   list[dict[str, Any]]],
          functools.partial(
              _multimodal_dict_output_fn,
              image_column,
              video_column,
              text_column)))


class VertexAIMultiModalEmbeddings(EmbeddingsManager):
  def __init__(
      self,
      model_name: str,
      image_column: Optional[str] = None,
      video_column: Optional[str] = None,
      text_column: Optional[str] = None,
      dimension: Optional[int] = None,
      project: Optional[str] = None,
      location: Optional[str] = None,
      credentials: Optional[Credentials] = None,
      **kwargs):
    """
    Embedding Config for Vertex AI Multi-Modal Embedding models following
    https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings # pylint: disable=line-too-long
    Multi-Modal Embeddings are generated for a batch of image, video, and
    string groupings using the Vertex AI API. Embeddings are returned in a list
    for each image in the batch as MultiModalEmbeddingResponses. This
    transform makes remote calls to the Vertex AI service and may incur costs
    for use.

    Args:
      model_name: The name of the Vertex AI Multi-Modal Embedding model.
      image_column: The column containing image data to be embedded. This data
        is expected to be formatted as VertexImage objects, containing a Vertex
        Image object.
      video_column: The column containing video data to be embedded. This data
        is expected to be formatted as VertexVideo objects, containing a Vertex
        Video object an a VideoSegmentConfig object.
      text_column: The column containing text data to be embedded. This data is
        expected to be formatted as Chunk objects, containing the string to be
        embedded in the Chunk's content field.
      dimension: The length of the embedding vector to generate. Must be one of
        128, 256, 512, or 1408. If not set, Vertex AI's default value is 1408.
        If submitting video content, dimension *musst* be 1408.
      project: The default GCP project for API calls.
      location: The default location for API calls.
      credentials: Custom credentials for API calls.
        Defaults to environment credentials.
    """
    self.model_name = model_name
    self.project = project
    self.location = location
    self.credentials = credentials
    self.kwargs = kwargs
    if dimension is not None and dimension not in (128, 256, 512, 1408):
      raise ValueError(
          "dimension argument must be one of 128, 256, 512, or 1408")
    self.dimension = dimension
    if not image_column and not video_column and not text_column:
      raise ValueError("at least one input column must be specified")
    if video_column is not None and dimension != 1408:
      raise ValueError(
          "Vertex AI does not support custom dimensions for video input, want dimension = 1408, got ",
          dimension)
    self.type_adapter = _create_multimodal_dict_adapter(
        image_column=image_column,
        video_column=video_column,
        text_column=text_column)
    super().__init__(type_adapter=self.type_adapter, **kwargs)

  def get_model_handler(self) -> ModelHandler:
    return _VertexAIMultiModalEmbeddingHandler(
        model_name=self.model_name,
        dimension=self.dimension,
        project=self.project,
        location=self.location,
        credentials=self.credentials,
        **self.kwargs)

  def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
    return RunInference(
        model_handler=_MultiModalEmbeddingHandler(self),
        inference_args=self.inference_args)
